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Empirical Approach to Machine Learning pp 295–319Cite as

Applications of Deep Rule-Based Classifiers

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 800))

Abstract

In this chapter, the algorithm summary of the main procedure of the deep rule-based (DRB) classifier described in Chap. 9 is provided. Numerical examples based on popular benchmark image sets including, handwritten digits recognition, remote sensing scene classification, face recognition and object recognition, etc., are presented for evaluating the performance of the DRB algorithm on image classification, and the state-of-the-art approaches are used for comparison. Numerical experiments show that DRB classifier is able to perform highly accurate classification in various image classification problems, and also demonstrate the advantages of its prototype-based nature and transparency over the existing approaches. The pseudo-code of the main procedure of the DRB classifier and the MATLAB implementations can be found in appendices B.5 and C.5, respectively.

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Authors and Affiliations

  1. School of Computing and Communications, Lancaster University, Lancaster, UK

    Plamen P. Angelov & Xiaowei Gu

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  1. Plamen P. Angelov
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  2. Xiaowei Gu
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Correspondence to Plamen P. Angelov .

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Angelov, P.P., Gu, X. (2019). Applications of Deep Rule-Based Classifiers. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-02384-3_13

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